llamacloud-python 0.1.1

Python client for LlamaCloud AI APIs

LlamaCloud Python LlamaCloud

A Python client for interacting with the LlamaCloud AI APIs for image and video generation.

Installation

pip install llamacloud-python

Usage

Authentication

You can authenticate using an API key directly or via an environment variable:

from llamacloud import LlamaCloud

# Option 1: API key directly
client = LlamaCloud(api_key="your_api_key", base_url="https://api.llamacloud.co")

# Option 2: Environment variable
# export LLAMA_CLOUD_API_KEY="your_api_key"
client = LlamaCloud()

Generating Images

# Generate an image
image = client.generate_image(
    model="glimmer-v1",
    prompt="a beautiful landscape",
    aspect_ratio=LlamaCloud.AspectRatio.LANDSCAPE_16_9,
    image_format=LlamaCloud.ImageFormat.PNG,
    seed=42
)

# Save the image
image.save("landscape")  # Saves as "landscape.png"

Generating Videos

# Generate a video
video = client.generate_video(
    model="wan-v1",
    prompt="a flowing river",
    quality=LlamaCloud.VideoQuality.HIGH,
    fps=30
)

# Save the video
video.save("river")  # Saves as "river.mp4"

API Reference

LlamaCloud

LlamaCloud(api_key=None, base_url="https://api.llamacloud.co", timeout=1200)

Creates a new client instance.

Parameters:

• api_key (Optional[str]): API key for authentication. If not provided, will attempt to use the LLAMA_CLOUD_API_KEY environment variable.
• base_url (str): Base URL for the API.
• timeout (int): Request timeout in seconds. Default is 1200 (20 minutes).

generate_image(model, prompt, aspect_ratio=AspectRatio.SQUARE, image_format=ImageFormat.WEBP, seed=None)

Generates an image based on the given prompt.

Parameters:

• model (str): The model to use for generation.
• prompt (str): The prompt describing the image.
• aspect_ratio (LlamaCloud.AspectRatio): The aspect ratio of the generated image.
• image_format (LlamaCloud.ImageFormat): The format of the generated image.
• seed (Optional[int]): Random seed for reproducibility.

Returns:

• Media: Media object containing the generated image.

generate_video(model, prompt, quality=LlamaCloud.VideQuality.HIGH, fps=25)

Generates a video based on the given prompt.

Parameters:

• model (str): The model to use for generation.
• prompt (str): The prompt describing the video.
• quality (LlamaCloud.VideoQuality): The quality of the video (LOW, QUALITY, HIGH).
• fps (int): Frames per second of the video.

Returns:

• Media: Media object containing the generated video.

Media

Media(base64_data, format)

Represents media data (images or videos).

Methods:

• save(path): Saves the media to the specified path. If no extension is provided, the correct one will be added based on the format.

Exceptions

APIError(status_code, message)

Raised when the API returns an error.

License

MIT

Development

Setup Development Environment

1. Clone the repository:

git clone https://github.com/brilliantai/llamacloud-python.git
cd llamacloud

2. Install development dependencies:

# Using pip
pip install -e . -r dev-requirements.txt

# Using uv (recommended)
uv pip install -e . -r dev-requirements.txt

Running Tests

Run tests with the provided script:

./scripts/run_tests.sh

Or run the commands individually:

# Run linting
ruff check .

# Run tests with coverage
pytest --cov=llamacloud

CI/CD

This project uses GitHub Actions for continuous integration and deployment:

• Tests are automatically run on all pull requests and pushes to the main branch
• When a new release is created, the package is automatically built and published to PyPI

Contributing

Contributions are welcome! Here's how you can contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests to ensure everything works
5. Commit your changes (git commit -m 'Add some amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Please make sure your code passes all tests and linting checks before submitting a pull request.